Dynamic connectedness between trading volumes and retail investor sentiment in the Russian stock market with Bitcoin during external shock periods
DOI:
https://doi.org/10.22394/1993-7601-2025-79-99-121Keywords:
переток ликвидности, рынок криптовалют, фондовый рынок, сентимент инвесторов, TVP-VARAbstract
The paper analyses the dynamic connectedness between trading volumes in the Russian stock market and the global cryptocurrency (Bitcoin) market during periods of external shocks (the COVID-19 crisis and the Russia-Ukraine conflict) from 2019. Using the TVP-VAR model and estimates of system variable connectivity, liquidity spillovers (cash flows) are identified between the considered asset classes. For the first time, we analyse the network connectedness of investor sentiment for the most popular Russian stocks and Bitcoin and estimate the role of sentiment in liquidity spillovers. The results demonstrate that Bitcoin's daily trading volume predominantly acts as a recipient of shocks from the Russian stock market. Meanwhile, the overall coherence in the system was higher during the COVID-19 crisis period compared to the Special Military Operation (SMO). The study confirms the existence of a correlation between investor sentiment on the Russian stock and cryptocurrency markets. At the beginning of the analyzed period, in 2019, when interest in crypto assets investing began to grow rapidly, this correlation was strongly pronounced. There is a connectedness between two sentiment indices: on the stock market and crypto-assets. The intensity of the correlation prevails in the COVID-19 inception period, significantly exceeding the level observed in the SMO period. This sentiment metrics behaviour may imply adaptation of the Russian stock market to external shocks, as well as retail investors' expectations of short-term SMO in early 2022.Downloads
References
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Manevich V. A., Peresetsky A. A., Pogorelova P. V. (2022). Stock market volatility and cryptocurrency volatility. Applied Econometrics, 65, 65–76 (in Russian). DOI: 10.22394/1993‑7601‑2022‑65‑65‑76.
Simonov A., Zyamalov V. (2019). Long Run return and survival factors of ICO. HSE Economic Journal, 23 (4), 585–604. DOI: 10.17323/1813‑8691‑2019‑23‑4‑585‑604.
Fantazzini D., Nigmatullin E., Sukhanovskaya V., Ivliev S. (2016). Everything you always wanted
to know about bitcoin modelling but were afraid to ask. Part I. Applied Econometrics, 44, 5–24 (in Russian).
Fantazzini D., Nigmatullin E., Sukhanovskaya V., Ivliev S. (2017). Everything you always wanted
to know about bitcoin modelling but were afraid to ask. Part 2. Applied Econometrics, 45, 5–28 (in Russian).
Abakah E. J. A., Adeabah D., Tiwari A. K., Abdullah M. (2023). Effect of Russia-Ukraine war sentiment on blockchain and FinTech stocks. International Review of Financial Analysis, 90, 102948. DOI: 10.1016/j.irfa.2023.102948.
Aftab M., Haq I. U., Albaity M. (2023). The COVID‑19 pandemic, economic policy uncertainty and the hedging role of cryptocurrencies: A global perspective. China Finance Review International, 13 (3), 471–508. DOI: 10.1108/CFRI-06-2022-0108.
Aganin A. D., Manevich V. A., Peresetsky A. A., Pogorelova P. V. (2023). Comparison of cryptocurrency and stock market volatility forecast models. HSE Economic Journal, 27 (1), 49–77 (in Russian). DOI: 10.17323/1813-8691-2023-27-1-49-77.
Ahmed Memon B., Aslam F., Naveed H. M., Ferreira P., Ganiev O. (2024). Influence of the Russia-Ukraine war and COVID‑19 pandemic on the efficiency and herding behavior of stock markets: Evidence from G20 nations. Economies, 12 (5), 106. DOI: 10.3390/economies12050106.
Antonakakis N., Chatziantoniou I., Gabauer D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13, 84. DOI: 10.3390/jrfm13040084.
Balcilar M., Gabauer D., Umar Z. (2021). Crude oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219. DOI: 10.1016/j.resourpol.2021.102219.
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Baruník J., Křehlík T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16 (2), 271–296. DOI: 10.1093/jjfinec/nby001.
Basdekis C., Christopoulos A., Katsampoxakis I., Nastas V. (2022). The impact of the Ukrainian war on stock and energy markets: A wavelet coherence analysis. Energies, 15 (21), 8174. DOI: 10.3390/en15218174.
Billio M., Getmansky M., Lo A. W., Pelizzon L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104 (3), 535–559. DOI: 10.1016/j.jfineco.2011.12.010.
Bossman A., Gubareva M. (2023). Asymmetric impacts of geopolitical risk on stock markets: A comparative analysis of the E7 and G7 equities during the Russian-Ukrainian conflict. Heliyon, 9 (2). DOI: 10.1016/j.heliyon.2023.e13626.
Broadstock D. C., Chatziantoniou I., Gabauer D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In: Applications in Energy Finance, 217–253. Cham: Springer International Publishing. DOI: 10.1007/978-3-030-92957-2_9.
Caferra R., Vidal-Tomás D. (2021). Who raised from the abyss? A comparison between cryptocurrency and stock market dynamics during the COVID‑19 pandemic. Finance Research Letters, 43, 101954. DOI: 10.1016/j.frl.2021.101954.
Cao G., Xie F. (2024). Extreme risk spillovers across energy and carbon markets: Evidence from the quantile extended joint connectedness approach. International Journal of Finance Economics, 29 (2), 2155–2175. DOI: 10.1002/ijfe.2781.
Chowdhury E. K., Chowdhury R., Dhar B. K. (2024). Understanding investor sentiment: Analyzing its influence on stock and cryptocurrency markets during the Russia-Ukraine war. Thunderbird International Business Review, 66 (5), 473–489. DOI: 10.1002/tie.22395.
Derindere Köseoğlu S., Mercangöz B. A., Khan K., Sarwar S. (2024). The impact of the Russian-Ukraine war on the stock market: A causal analysis. Applied Economics, 56 (21), 2509–2519. DOI: 10.1080/00036846.2023.2188168.
Diebold F. X., Yılmaz K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182 (1), 119–134. DOI: 10.1016/j.jeconom.2014.04.012.
Fantazzini D., Nigmatullin E., Sukhanovskaya V., Ivliev S. (2016). Everything you always wanted to know about bitcoin modelling but were afraid to ask. Part I. Applied Econometrics, 44, 5–24 (in Russian).
Fantazzini D., Nigmatullin E., Sukhanovskaya V., Ivliev S. (2017). Everything you always wanted to know about bitcoin modelling but were afraid to ask. Part 2. Applied Econometrics, 45, 5–28 (in Russian).
Forbes K. J., Rigobon R. (2002). No contagion, only interdependence: Measuring stock market comovements. Journal of Finance, 57 (5), 2223–2261. DOI: 10.1111/0022-1082.00494.
Frikha W., Brahim M., Jeribi A., Lahiani A. (2023). COVID‑19, Russia-Ukraine war and interconnectedness between stock and crypto markets: A wavelet-based analysis. Journal of Business Analytics, 6 (4), 255–275. DOI: 10.1080/2573234X.2023.2193224.
Fuentes F., Herrera R. (2020). Dynamics of connectedness in clean energy stocks. Energies, 13 (14), 3705. DOI: 10.3390/en13143705.
Ghorbel A., Loukil S., Bahloul W. (2022). Connectedness between cryptocurrencies, gold and stock markets in the presence of the COVID‑19 pandemic. European Journal of Management and Business Economics, 33 (4), 466–487. DOI: 10.1108/EJMBE-10-2021-0281.
Ha L. T. (2023a). Dynamic interlinkages between the crude oil and gold and stock during Russia-Ukraine war: Evidence from an extended TVP-VAR analysis. Environmental Science and Pollution Research, 30 (9), 23110–23123. DOI: 10.1007/s11356-022-23456-0.
Ha L. T. (2023b). Interlinkages of cryptocurrency and stock markets during COVID‑19 pandemic by applying a TVP-VAR extended joint connected approach. Journal of Economic Studies, 50 (3), 407–428. DOI: 10.1108/JES-01-2022-0055.
Hudson R., Urquhart A. (2015). War and stock markets: The effect of World War Two on the British stock market. International Review of Financial Analysis, 40, 166–177. DOI: 10.1016/j.irfa.2015.05.015.
Izzeldin M., Muradoğlu Y. G., Pappas V., Petropoulou A., Sivaprasad S. (2023). The impact of the Russian-Ukrainian war on global financial markets. International Review of Financial Analysis, 87, 102598. DOI: 10.1016/j.irfa.2023.102598.
Jana S., Sahu T. N. (2023). Can diversification be improved by using cryptocurrencies? Evidence from Indian equity market. Journal of Financial Economic Policy, 15 (6), 551–573. DOI: 10.1108/JFEP-02-2023-0047.
Jeribi A., Jena S. K., Lahiani A. (2021). Are cryptocurrencies a backstop for the stock market in a COVID‑19-led financial crisis? Evidence from the NARDL approach. International Journal of Financial Studies, 9 (3), 33. DOI: 10.3390/ijfs9030033.
Koop G., Korobilis D. (2014). A new index of financial conditions. European Economic Review, 71, 101–116. DOI: 10.1016/j.euroecorev.2014.07.002.
Kroner K. F., Sultan J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28 (4), 535–551. DOI: 10.2307/2331164.
Le Bris D. (2012). Wars, inflation and stock market returns in France, 1870–1945. Financial History Review, 19 (3), 337–361. DOI: 10.1017/S0968565012000170.
Le V. H., von Mettenheim H. J., Goutte S., Liu F. (2023). News-based sentiment: Can it explain market performance before and after the Russia-Ukraine conflict? The Journal of Risk Finance, 24 (1), 72–88. DOI: 10.1108/JRF-06-2022-0168.
Maitra D., Rehman M. U., Dash S. R., Kang S. H. (2022). Do cryptocurrencies provide better hedging? Evidence from major equity markets during COVID‑19 pandemic. The North American Journal of Economics and Finance, 62, 101776. DOI: 10.1016/j.najef.2022.101776.
Majumder S. B. (2022). Searching for hedging and safe haven assets for Indian equity market — a comparison between gold, cryptocurrency and commodities. Indian Growth and Development Review, 15 (1), 60–84. DOI: 10.1108/IGDR-10-2021-0131.
Manevich V. A., Peresetsky A. A., Pogorelova P. V. (2022). Stock market volatility and cryptocurrency volatility. Applied Econometrics, 65, 65–76 (in Russian). DOI: 10.22394/1993-7601-2022-65-65-76.
Mensi W., Shafiullah M., Vo X. V., Kang S. H. (2021). Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies. Resources Policy, 71, 102002. DOI: 10.1016/j.resourpol.2021.102002.
Naeem M. A., Farid S., Qureshi F., Taghizadeh-Hesary, F. (2023). Global factors and the transmission between United States and emerging stock markets. International Journal of Finance Economics, 28 (4), 3488–3510. DOI: 10.1002/ijfe.2604.
Pesaran H. H., Shin Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58 (1), 17–29. DOI: 10.1016/S0165-1765(97)00214-0.
Polat O., Başar B. D., Ekşi İ. H. (2024). Dynamic interlinkages between the Twitter Uncertainty Index and the Green Bond market: Evidence from the Covid‑19 pandemic and the Russian-Ukrainian conflict. Computational Economics, 1–17. DOI: 10.1007/s10614-024-10666-6.
Qiu Y., Song Z., Chen Z. (2022). Short-term stock trends prediction based on sentiment analysis and machine learning. Soft Computing, 26 (5), 2209–2224. DOI: 10.1007/s00500-021-06602-7.
Simonov A., Zyamalov V. (2019). Long Run return and survival factors of ICO. HSE Economic Journal, 23 (4), 585–604. DOI: 10.17323/1813-8691-2019-23-4-585-604.
Singh A., Patel R., Singh H. (2022). Recalibration of priorities: Investor preference and Russia-Ukraine conflict. Finance Research Letters, 50, 103294. DOI: 10.1016/j.frl.2022.103294.
Sul H. K., Dennis A. R., Yuan L. (2017). Trading on twitter: Using social media sentiment to predict stock returns. Decision Sciences, 48 (3), 454–488. DOI: 10.1111/deci.12229.
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